所属单位：东北大学
教研室：过程工业综合自动化国家重点实验室
发表刊物：Information Sciences
刊物所在地：ELSEVIER Inc.
项目来源：中国国家自然科学基金, 项目号: 61290323, 61333007, 61473064; IAPI 基础研究基金, 项目号: 2013ZCX02-09.
关键字：Online sequential random vector, functional-link networks, Molten iron quality.
摘要：This paper presents a data-driven dynamic modeling method for the multivariate prediction of molten iron quality (MIQ) in a blast furnace (BF)using online sequential random vector functional-link networks (OS-RVFLNs)with the help of principal component analysis (PCA). At first, a data-driven PCA is employed to identify the most influential components from mul-titudinous factors that affect MIQ so as to reduce the model dimension. Secondly, a dynamic OS-RVFLNs modeling technology with fast learning and strong nonlinear mapping capability is proposed.
备注：SCI, WOS: 000362380600015; EI, Accession Number: 20153801283023.
合写作者：Tian-You Chai,王卓, Hong Wang, Meng Yuan,Ping Zhou*
论文类型：基础研究
文献类型：期刊
是否译文：否
发表时间：2015-07-09